package tableapi;

import bean.SensorReading;
import org.apache.flink.streaming.api.TimeCharacteristic;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.table.api.Over;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.Tumble;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;

public class TableTest5_TimeAndWindow {

    public static void main(String[] args) throws Exception {

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);

        DataStreamSource<String> inputStream = env.readTextFile("D:\\CS\\MyDemo\\gmall-flink\\flink-demo\\src\\main\\resources\\sensor.txt");

        SingleOutputStreamOperator<SensorReading> dataStream = inputStream.map(line -> {
            String[] fields = line.split(",");
            return new SensorReading(fields[0], new Long(fields[1]), new Double(fields[2]));
        }).assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<SensorReading>(Time.seconds(2)) {
            @Override
            public long extractTimestamp(SensorReading sensorReading) {
                return (long) (sensorReading.getTemperature()*1000L);
            }
        });

        // 将流转换成表，定义时间特性
        Table dataTable = tableEnv.fromDataStream(dataStream, "id, timestamp as ts, temperature as temp, rt.rowtime");
        tableEnv.createTemporaryView("sensor",dataTable);

        // 1 窗口操作
        // Group Window
        // table API
        Table resultTable = dataTable.window(Tumble.over("10.seconds").on("rt").as("tw"))
                .groupBy("id,tw")
                .select("id, id.count, temp.avg, tw.end");

        // SQL
        Table resultSqlTable = tableEnv.sqlQuery("select id, count(id) as cnt, avg(temp) as avgTemp, tumble_end(rt, interval '10' second) " +
                "from sensor group by id, tumble(rt, interval '10' second)");




        // 2 Over Window
        // table API
        Table overResult = dataTable.window(Over.partitionBy("id").orderBy("rt").preceding("2.rows").as("ow"))
                .select("id, rt, id.count over ow, temp.avg over ow");

        // SQL
        Table overSqlResult = tableEnv.sqlQuery("select id, rt, count(id) over ow, avg(temp) over ow " +
                " from sensor " +
                " window ow as (partition by id order by rt rows between 2 preceding and current row)");

//        tableEnv.toAppendStream(resultSqlTable, Row.class).print();
        tableEnv.toRetractStream(overSqlResult, Row.class).print("sql");
        env.execute();


    }
}
